The function generates a histogram-like reorganisation of the data, to assess counts per bin. The log-transformed counts per bin are used to calculate the second derivative of the data (i.e., the curvature of the curve) and to find the central value of the bin hosting the distribution maximum. A normal distribution model is fitted to the counts per bin data to estimate the dose distribution parameters. The uncertainty of the model is estimated based on all input equivalent doses smaller that of the modelled central value.

`calc_WodaFuchs2008(data, breaks = NULL, plot = TRUE, ...)`

data

data.frame or '>RLum.Results object (**required**):
for data.frame: two columns: De (`values[,1]`

) and De error (`values[,2]`

).
For plotting multiple data sets, these must be provided as `list`

(e.g. `list(dataset1, dataset2)`

).

breaks

numeric:
Either number or locations of breaks. See `[hist]`

for details.
If missing, the number of breaks will be estimated based on the bin width
(as function of median error).

plot

logical (*with default*):
enable plot output.

...

Further plot arguments passed to the function.

0.2.0 (2018-02-08 14:24:10)

Kreutzer, S., Dietze, M. (2018). calc_WodaFuchs2008(): Obtain the equivalent dose using the approach by Woda and Fuchs 2008. Function version 0.2.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6. https://CRAN.R-project.org/package=Luminescence

Woda, C., Fuchs, M., 2008. On the applicability of the leading edge method to obtain equivalent doses in OSL dating and dosimetry. Radiation Measurements 43, 26-37.

# NOT RUN { ## read example data set data(ExampleData.DeValues, envir = environment()) results <- calc_WodaFuchs2008( data = ExampleData.DeValues$CA1, xlab = expression(paste(D[e], " [Gy]")) ) # }